Job qualifications study for data science and big data professions

Abstract

PurposeThe lack of clarity in defining data science is problematic in both academia and industry because the former has a need for clarity to establish curriculum guidelines in their work to prepare future professionals, and the latter has a need for information to establish clear job description guidelines to recruit professionals. This lack of clarity has resulted in job descriptions with significant overlap among different related professional groups. This study examines the industry view of five professions: statistical analysts (SAs), big data analytics professionals (BDAs), data scientists (DSs), data analysts (DAs) and business analytics professionals (BAs). The study compares the five fields with the unified backdrop of their common semantic dimensions and examines their recent dynamics.Design/methodology/approach1,200 job descriptions for the five Big Data professions (SA, DS, BDA, DA and BA) were pulled from the Monster website at four points in time, and a document library was created. The collected job qualification records were analyzed using the text analytic method of Latent Semantic Analysis (LSAs), which extract topics based on observed text usage patterns.FindingsThe findings indicated a good alignment between the industry view and the academic view of data science as a blend of statistical and programming skills. This industry view remained relatively stable during the 4 years of our study period.Originality/valueThis research paper builds upon a long tradition of related studies and commentaries. Rather than relying on subjective expertise, this study examined the job market and used text analytics to discern a space of skill and qualification dimensions from job announcements related to five big data professions.

Editorial: Perspectives on the value of Big Data sharing

“…The skills required for data science and for any activity aiming to create and capture value from Big Data would require further empirical research, elaborating on the elements and sources considered in the paper by Halwani et al (2022) in this special section. Clearly, from the point of view of organizations in both the public and private sectors, legacy skills are not immediately substitutable by digital skills nor should they necessarily be (cf.…”

“…Finally, the article by Halwani et al (2022), “Job qualifications study for data science and Big Data professions,” provides an interesting analysis of the different skills required for data science and other professional profiles related to the exploration and exploitation of Big Data analytics. Besides providing an empirical perspective on the existing portfolio of data science skills and capabilities, the authors' findings show that a good alignment exists between the industry view and the academic view on what is needed for performing data science, blending statistical and programming skills.…”

Requisitos para a ciência de dados: analisando anúncios de vagas de emprego com mineração de texto

“…Novamente, dados, machine learning, análise, experiência, conhecimento, tecnologia, modelagem estatística, entendimento de negócio, projeto, desenvolvimento (software) e busca pelo novo foram termos que mais representam os requisitos para a ciência de dados. Esse resultado vai ao encontro de pesquisas preliminares como Kim e Lee (2016), Meyer (Meyer, 2019), Halwani et al (2021) e Gottipati et al (2021), por exemplo. Em contrapartida, diferentemente do que outras pesquisas apontam, não foi possível identificar a frequência de habilidade interpessoais, como comunicação oral e escrita (Baumeister et al, 2020;Cao, 2019;Kim & Lee, 2016) ou conhecimentos referentes a questões sociais, éticas e legais relacionadas a privacidade e segurança de dados (Anderson et al, 2014;Curty & Serafim, 2016).…”

“…É reconhecido que não há no mercado número suficiente de pessoas qualificadas para desenvolver projetos que envolvam técnicas complexas de análise, como machine learning e deep learning, nem para implementar mudanças organizacionais rumo a uma cultura orientada a dados (Cunha, 2018;Hall et al, 2016). Para agravar a situação, como a ciência de dados é uma área em desenvolvimento, não há entendimento ou clareza terminológica acerca das descrições dos diferentes profissionais desse cenário (Halwani et al, 2021). Esse fato traz prejuízos para todos os atores envolvidos: empresas, instituições de ensino e, claro, os próprios indivíduos.…”

“…Aumentar o entendimento sobre os requisitos e, por consequência, das competências dos profissionais de dados beneficia o mercado de trabalho, quem será empregado, a academia, mas também os profissionais que atuam na formação em ciência de dados (Halwani et al, 2021). Nesse sentido, espera-se que, ao responder as perguntas acima, a pesquisa possa contribuir com essa área que se mostra cada vez mais importante à sociedade.…”